iPC EU Horizon 2020 - Individualized Paediatric Cure: Cloud-based Virtual-patient Models for Precision Paediatric Oncology

The objective of this proposed research is to advance the field of unsupervised visual anomaly detection (UAD), with a focus on medical imagery. Unsupervised visual anomaly detection is a data analysis method used to model and detect previously unknown anomalies in data, streamlining the process of tumor research and treatment development. Developed UAD methods will be evaluated on all the available medical imagery available as part of the Pediatric Brain Tumor Atlas such as histology images, radiology images, and MR images. In comparison with existing supervised approaches, unsupervised approaches will be widely applicable to the vast amount of existing medical imagery data. Research will be performed as part of the iPC EU Horizon 2020 project1, where one of the main objectives of the proposed research is to support individualized diagnostics and treatment for pediatric brain cancer patients.